Methods for forecasting, planning, and analysis for contact processing centers are important for increasing the efficiency of contact processing centers. These methods may include regression modeling, queuing equation modeling, and discrete-event simulation modeling. However, all of these methods are either of limited applicability, unacceptably inaccurate, or difficult to use for contact center forecasting, planning, and analysis.
Regression methods predict contact center behavior based on the behavior observed in the historical dataset used to build the regression model. As a result, regression is best suited to large contact centers with stable workflows, consistent customer behavior, and slowly changing or invariant contact volumes. However, contact centers with these characteristics are rare, so the utility of regression is limited. In addition, because regression is only generally accurate when workflows are stable, regression cannot be used to conduct many kinds of analysis necessary for effective contact center management (e.g., understanding the impacts of center expansion, consolidation, or process-changing technology investments).
Queuing equation-based methods (generally embedded in contact center workforce management systems) are the most widely used methods for forecasting, planning, and analysis in contact centers. While workforce management systems are primarily used for short-term scheduling and personnel management, they can also be employed for forecasting, planning, and analysis purposes.
Workforce management systems generally include methods for forecasting workloads as well as automatic and manual methods for assigning employees to work-shifts in order to meet service quality goals. In these systems, queuing equations are used to estimate the personnel (i.e., agents or servers) required for each hour of the day to service a given workload. The most widely used queuing equations in contact centers are the so-called “Erlang” equations.
However, workforce management systems, and the Erlang equations that drive them, are deficient when used for planning and analysis purposes for two main reasons. First, Erlang equations require simplifying assumptions that are not appropriate in modern contact centers, and that result in inaccurate forecasts of center behavior. Second, since workforce management systems are not designed for forecasting, planning, and analysis, using them for these functions is cumbersome and time-consuming.
Erlang equations require three significant simplifying assumptions that limit their forecast accuracy. First, the Erlang equations assume no callers abandon i.e., they assume that no caller hangs up if forced to wait an excessive amount of time for an agent. This assumption can lead to gross inaccuracies in forecast performance under certain conditions. In addition, since the number of abandons has a direct bearing on the enterprise's revenue and customer satisfaction, the use of forecast methods that ignore abandons makes it difficult to properly manage and improve the revenue and customer satisfaction performance of the enterprise.
Second, Erlang assumes all contacts are handled in the same way, ignoring any tailored contact center workflows and contact routing strategies. However, such simple configurations are no longer realistic given advancements in contact center technology such as interactive voice response units (IVRs), “virtual queues”, load balancing, and skill-based routing. This assumption can also lead to gross inaccuracies in forecast performance under certain conditions.
Third, Erlang equations do not calculate contact center sales, profit, and other metrics. Because of this limitation, contact center managers currently typically base their forecasts, plans, and management strategy on metrics (e.g., service level) that have little relation to the ultimate profitability of their businesses. This curious aspect of contact center management has arisen simply because the limitations of the analytic tools currently available.
In addition to the limitations associated with their Erlang-based forecasting methods, the usefulness of workforce management systems for forecasting, planning, and analysis is limited because they are designed for short-term tactical workforce scheduling and employee management purposes. Current systems do not easily support the rapid evaluation of strategic business issues such as sizing new contact centers, consolidating contact centers, determining the effect on profit and sales of hiring decisions, or developing contact center budgets and staff plans. Additionally, current systems use methodologies that do not allow for easy calculation of the distribution of customer experience (i.e., the proportions of the customer base that experiences differing levels of service quality) and the distribution of contact center performance. Finally, workforce management systems are not designed for iterative “what-if” analysis of the impact of changes in resource levels and service quality goals on staffing requirements, cost, and profit. Using these systems for scenario analysis is cumbersome and slow.
Techniques for building discrete-event simulations of contact center processing operations for planning and analysis purposes are also known. Such methods allow analysts to build a model of the contact center to estimate the impact on center performance of changes to performance drivers (e.g., call arrivals, staffing levels, and call routing policies).
These techniques to build discrete-event simulations can be useful for doing “one-off” analyses (such as center consolidation), but have severe practical disadvantages in day-to-day use. Typically, these techniques suffer from drawbacks opposite to those of queuing-based methods. While queuing-based methods are fast, easy to use, but overly simplistic, simulation systems are, in contrast, typically precise and flexible (e.g., they can be customized to the specific configuration and routing strategies of the contact center) but complex to develop, difficult to use, and too computationally slow for budget and staff planning. For example, developing “what-if” analysis using simulation models is tedious, as each set of input parameters is typically manually entered into the model, the model run (which may take several hours or more) and the results evaluated.
Given the current limitations of the existing techniques, workforce and financial planners need a system that can easily produce accurate staff and budget plans as well as comprehensive contact center behavioral analysis.